Current Evidence, Challenges, and Opportunities of Physiologically Based Pharmacokinetic Models of Atorvastatin for Decision Making

Abstract

Atorvastatin (ATS) is the gold-standard treatment worldwide for the management of hypercholesterolemia and prevention of cardiovascular diseases associated with dyslipidemia. Physiologically based pharmacokinetic (PBPK) models have been positioned as a valuable tool for the characterization of complex pharmacokinetic (PK) processes and its extrapolation in special sub-groups of the population, leading to regulatory recognition. Several PBPK models of ATS have been published in the recent years, addressing different aspects of the PK properties of ATS. Therefore, the aims of this review are (i) to summarize the physicochemical and pharmacokinetic characteristics involved in the time-course of ATS, and (ii) to evaluate the major highlights and limitations of the PBPK models of ATS published so far. The PBPK models incorporate common elements related to the physicochemical aspects of ATS. However, there are important differences in relation to the analyte evaluated, the type and effect of transporters and metabolic enzymes, and the permeability value used. Additionally, this review identifies major processes (lactonization, P-gp contribution, ATS-Ca solubility, simultaneous management of multiple analytes, and experimental evidence in the target population), which would enhance the PBPK model prediction to serve as a valid tool for ATS dose optimization.

Atorvastatin population pharmacokinetics in a real‐life setting: Influence of genetic polymorphisms and association with clinical response

“…With few exceptions, the PK studies reported to date consist mainly in classical association studies using noncompartmental PK analysis sometimes performed on healthy volunteers after single dose administration 21,28,32,[35][36][37] or physiologically-based PK models. [40][41][42][43][44] Some population PK (PopPK) studies have been published but with strict, rich sampling and/or in a limited number of (healthy) individuals without considering genetic polymorphisms and, thus, are not likely to reflect the constraints of real-life clinical settings. 29,[45][46][47][48] In this context, with the support of rich datasets from former studies, 49,50 our study aimed at constructing a PopPK model with data prospectively collected in a cohort of ambulatory patients sparsely sampled and including relevant pharmacogenetic information.…”

Platelet thromboxane inhibition by low‐dose aspirin in polycythemia vera: Ex vivo and in vivo measurements and in silico simulation

“…9 PK/ PD modeling and simulation are central in informing and personalizing therapy in situations in which gaps in data are expected, such as in rare diseases, [37][38][39] in case of alternative dosing regimens thus repurposing approved drugs. 37,40,41 Thus, the results of the present study may help redefining the current recommendation of a b.i.d. regimen for a selected patient with PV subset (i.e., those with inadequate serum TXB 2 suppression), rather than for the entire PV population.…”

“…The in silico modeling of patients with PV with higher TXA 2 production required a comparable increase in the COX‐1 biosynthetic rate as in the ET condition, 9,21 whereas patients with PV with lower platelet TXA 2 production could be modeled by the same parameters of aspirin‐treated non‐PV patients with a presumably normal bone marrow function (Figure 4). 9 PK/PD modeling and simulation are central in informing and personalizing therapy in situations in which gaps in data are expected, such as in rare diseases, 37–39 in case of alternative dosing regimens thus repurposing approved drugs 37,40,41 . Thus, the results of the present study may help redefining the current recommendation of a b.i.d.…”

Physiologically based pharmacokinetic modelling and simulation to predict the plasma concentration profile of schaftoside after oral administration of total flavonoids of Desmodium styracifolium

“…Physiologically based pharmacokinetic (PBPK) modeling is a mathematical modeling technique to describe and predict drug disposition in various populations ( Jones and Rowland-Yeo, 2013 ). By integrating population-specific physiologic parameters with drug-specific physicochemical and pharmacokinetics information, PBPK models are increasingly used for the prediction of drug distribution, drug-drug interactions (DDI), transporter evaluation, and extrapolation of drug exposure in age-specific or special subgroups of patients ( Jones et al, 2009 ; Maharaj and Edginton, 2014 ; Reig-Lopez et al, 2021 ). However, the application of PBPK modeling for the active ingredients of herbal medicine was limitedly reported.…”